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arXiv2021

Assessment of Neural Networks for Stream-Water-Temperature Prediction

Climate change results in altered air and water temperatures. Increases affect physicochemical properties, such as oxygen concentration, and can shift species distribution and survival, with consequences for ecosystem functioning and services. These ecosystem services have integral value for humankind and are forecasted to alter under climate warming. A mechanistic understanding of the drivers and magnitude of expected changes is essential in identifying system resilience and mitigation measures. In this work, we present a selection of state-of-the-art Neural Networks (NN) for the prediction of water temperatures in six streams in Germany. We show that the use of methods that compare observed and predicted values, exemplified with the Root Mean Square Error (RMSE), is not sufficient for their assessment. Hence we introduce additional analysis methods for our models to complement the state-of-the-art metrics. These analyses evaluate the NN's robustness, possible maximal and minimal values, and the impact of single input parameters on the output. We thus contribute to understanding the processes within the NN and help applicants choose architectures and input parameters for reliable water temperature prediction models.

cs.LG↗
arXiv2021

Empirical estimation of anthropogenic and natural contributions to surface air temperature trends at different latitudes

How strong are quantitative contributions of the key natural modes of climate variability and the anthropogenic factor characterized by the changes of the radiative forcing of greenhouse gases in the atmosphere to the trends of the surface air temperature at different latitudes of the Northern and Southern Hemispheres on various time intervals? Such contributions to trends are estimated here from observation data with the simplest empirical models. Trivariate autoregressive models are fitted to the data since the 19th century and used to assess the impact of the anthropogenic forcing together with different natural climate modes including Atlantic Multidecadal Oscillation, El-Nino / Southern Oscillation, Interdecadal Pacific Oscillation, Pacific Decadal Oscillation, and Antarctic Oscillation. For relatively short intervals of the length of two or three decades, we note considerable contributions of the climate variability modes which are comparable to the contributions of the greenhouse gases and even exceed the latter. For longer intervals of about half a century and greater, the contributions of greenhouse gases dominate at all latitudes as follows from the present analysis of data for polar, middle and tropical regions.

physics.ao-ph↗
arXiv2022

Surrogate Ensemble Forecasting for Dynamic Climate Impact Models

As acute climate change impacts weather and climate variability, there is increased demand for robust climate impact model predictions from which forecasts of the impacts can be derived. The quality of those predictions are limited by the climate drivers for the impact models which are nonlinear and highly variable in nature. One way to estimate the uncertainty of the model drivers is to assess the distribution of ensembles of climate forecasts. To capture the uncertainty in the impact model outputs associated with the distribution of the input climate forecasts, each individual forecast ensemble member has to be propagated through the physical model which can imply high computational costs. It is therefore desirable to train a surrogate model which allows predictions of the uncertainties of the output distribution in ensembles of climate drivers, thus reducing resource demands. This study considers a climate driven disease model, the Liverpool Malaria Model (LMM), which predicts the malaria transmission coefficient R0. Seasonal ensembles forecasts of temperature and precipitation with a 6-month horizon are propagated through the model, predicting the distribution of transmission time series. The input and output data is used to train surrogate models in the form of a Random Forest Quantile Regression (RFQR) model and a Bayesian Long Short-Term Memory (BLSTM) neural network. Comparing the predictive performance, the RFQR better predicts the time series of the individual ensemble member, while the BLSTM offers a direct way to construct a combined distribution for all ensemble members. An important element of the proposed methodology is that accounting for non-normal distributions of climate forecast ensembles can be captured naturally by a Bayesian formulation.

cs.LG↗
arXiv2022

Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network

Satellite-derived measurements are negatively impacted by cloud cover and surface reflectivity. These biases must be discarded and significantly increase the amount of missing data within remote sensing images. This paper expands the application of a partial convolutional neural network (PCNN) to incorporate depthwise convolution layers, conferring temporal dimensionality to the imputation process. The addition of a temporal dimension to the imputation process adds a state of successive existence within the dataset which spatial imputation cannot capture. The depthwise convolution process enables the PCNN to independently convolve the data for each channel. The deep learning system is trained with the Community Multiscale Air Quality model-simulated tropospheric column density of Nitrogen Dioxide (TCDNO2) to impute TROPOspheric Monitoring Instrument TCDNO2. The depthwise PCNN model achieves an index of agreement of 0.82 and outperforms the default PCNN models, with and without temporal dimensionality of data, and conventional data imputation methods such as inverse distance weighting by 3-11% and 8-15% in the index of agreement and correlation, respectively. The model demonstrates more consistency in the reconstruction of TROPOspheric Monitoring Instrument tropospheric column density of NO2 images. The model has also demonstrated the accurate imputation of remote sensing images with over 95% of the data missing. PCNN enables the accurate imputation of remote sensing data with large regions of missing data and will benefit future researchers conducting data assimilation for numerical models, emission studies, and human health impact analyses from air pollution.

physics.ao-ph↗
arXiv2022

Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting

Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.

eess.SP↗
arXiv2024

Atmospheric Transport Modeling of CO$_2$ with Neural Networks

Accurately describing the distribution of CO$_2$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems to aid implementation of international climate agreements. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. While similar in this regard, atmospheric transport modeling is subject to new challenges. Both, stable predictions for longer time horizons and mass conservation throughout need to be achieved, while IO plays a larger role compared to computational costs. In this study we explore four different deep neural networks (UNet, GraphCast, Spherical Fourier Neural Operator and SwinTransformer) which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling. For this, we assemble the CarbonBench dataset, a systematic benchmark tailored for machine learning emulators of Eulerian atmospheric transport. Through architectural adjustments, we decouple the performance of our emulators from the distribution shift caused by a steady rise in atmospheric CO$_2$. More specifically, we center CO$_2$ input fields to zero mean and then use an explicit flux scheme and a mass fixer to assure mass balance. This design enables stable and mass conserving transport for over 6 months with all four neural network architectures. In our study, the SwinTransformer displays particularly strong emulation skill (90-day $R^2 > 0.99$), with physically plausible emulation even for forward runs of multiple years. This work paves the way forward towards high resolution forward and inverse modeling of inert trace gases with neural networks.

cs.LG↗
arXiv2026

The Localized 12-hour Wave Over Alaska: Leveraging Meridional Wind Measurements From the Sodium Lidar

The 12-h wave in meridional winds in the mesosphere and lower thermosphere (MLT) during the solar minimum 2018-2019 Arctic winter is investigated using sodium lidar observations at Poker Flat Research Range (PFRR), Chatanika, Alaska (64N,147W). Nightly 12-h wave amplitudes increased significantly during December-January, with amplitudes exceeding 130 m/s above 97 km on several days. This was more than double the 12-h wave amplitudes observed outside this time period. Meteor radar winds over Chatanika also showed significant increase in meridional wind 12-h wave amplitudes at altitudes between 82 and 97 km during this time period with lower amplitudes than lidar measurements. The strong variation in 12-h wave amplitudes was not correlated with SME index, though the largest amplitudes coincided with the sudden stratospheric warming (SSW) in early January. Measurements were compared to a seasonal WACCM-X model run and four days of HIAMCM. For the four dates of available HIAMCM data, 12-h wave amplitudes over Chatanika were found to be similar between both HIAMCM and WACCM-X and the lidar below 97 km, with amplitudes measured by lidar exceeding the models at altitudes between 97-105 km. All measurements followed a similar seasonal trend with increasing amplitudes at the end of December/early January. Fits of SW2 from WACCM-X show the SW2 tidal amplitude following similar seasonal trends to 12-h wave measurements. These high-resolution lidar measurements indicate that localized 12-h wave amplitudes are larger than previously reported by studies using meteor radar measurements.

physics.space-ph↗
arXiv2014

Global Climate Modeling of the Martian water cycle with improved microphysics and radiatively active water ice clouds

Radiative effects of water ice clouds have noteworthy consequences on the Martian atmosphere, its thermal structure and circulation. Accordingly, the inclusion of such effects in the LMD Mars Global Climate Model (GCM) greatly modifies the simulated Martian water cycle. The intent of this paper is to address the impact of radiatively active clouds on atmospheric water vapor and ice in the GCM and improve its representation. We propose a new enhanced modeling of the water cycle, consisting of detailed cloud microphysics with dynamic condensation nuclei and a better implementation of perennial surface water ice. This physical modeling is based on tunable parameters. This new version of the GCM is compared to the Thermal Emission Spectrometer observations of the water cycle. Satisfying results are reached for both vapor and cloud opacities. However, simulations yield a lack of water vapor in the tropics after Ls=180° which is persistent in simulations compared to observations, as a consequence of aphelion cloud radiative effects strengthening the Hadley cell. Every year, our GCM simulations indicate that permanent surface water ice on the north polar cap increases at latitudes higher than 80°N and decreases at lower latitudes. Supersaturation above the hygropause is predicted in line with SPICAM observations. The model also shows for the first time that the scavenging of dust by water ice clouds alone fails to fully account for observed dust detached layers.

astro-ph.EP↗
arXiv2014

Wind and Wave Extremes over the World Oceans from Very Large Ensembles

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the dataset. The period (9 yrs) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for non-parametric 100-yr return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.

physics.ao-ph↗
arXiv2014

Self-organization of the Earths climate system versus Milankovitch-Berger astronomical cycles

The Late Pleistocene Antarctic temperature variation curve is decomposed into two components: cyclic and high frequency, stochastic. For each of these components, a mathematical model is developed which shows that the cyclic and stochastic temperature variations are distinct, but interconnected, processes with their own self-organization. To model the cyclic component, a system of ordinary differential equations is written which represent an auto-oscillating, self-organized process with constant period. It is also shown that these equations can be used to model more realistic variations in temperature with changing cycle length. For the stochastic component, the multifractal spectrum is calculated and compared to the multifractal spectrum of a critical sine-circle map. A physical interpretation of relevant mathematical models and discussion of future climate development within the context of this work is given.

physics.ao-ph↗
arXiv2017

Solar-driven variation in the atmosphere of Uranus

Long-term measurements (1972-2015) of the reflectivity of Uranus at 472 and 551 nm display variability that is incompletely explained by seasonal effects. Spectral analysis shows this non-seasonal variability tracks the 11-year solar cycle. Two mechanisms could cause solar modulation, (a) nucleation onto ions or electrons created by galactic cosmic rays (GCR), or (b) UV-induced aerosol colour changes. Ion-aerosol theory is used to identify expected relationships between reflectivity fluctuations and GCR flux, tested with multiple regression and compared to the linear response predicted between reflectivity and solar UV flux. The statistics show that 24%of the variance in reflectivity fluctuations at 472 nm is explained by GCR ion-induced nucleation, compared to 22% for a UV-only mechanism. Similar GCR-related variability exists in Neptune's atmosphere, hence the effects found at Uranus provide the first example of common variability in two planetary atmospheres driven through energetic particle modulation by their host star.

physics.ao-ph↗
arXiv2013

Investigation of cosmic ray-cloud connections using MISR

Numerous empirical studies have analyzed International Satellite Cloud Climatology Project data and reached contradictory conclusions regarding the influence of solar-modulated galactic cosmic rays on cloud fraction and cloud properties. The Multiangle Imaging Spectroradiometer (MISR) instrument on the Terra satellite has been in continuous operation for 13 years and thus provides an independent (and previously unutilized) cloud data set to investigate purported solar-cloud links. Furthermore, unlike many previous solar-climate studies that report cloud fraction MISR measures albedo, which has clearer climatological relevance. Our long-term analysis of MISR data finds no statistically significant correlations between cosmic rays and global albedo or globally averaged cloud height, and no evidence for any regional or lagged correlations. Moreover, epoch superposition analysis of Forbush decreases reveals no detectable albedo response to cosmic ray decreases, thereby placing an upper limit on the possible influence of cosmic ray variations on global albedo of 0.0029 per 5% decrease. The implications for recent global warming are discussed.

physics.ao-ph↗
arXiv2013

Efficient particle filtering through residual nudging

We introduce an auxiliary technique, called residual nudging, to the particle filter to enhance its performance in cases that it performs poorly. The main idea of residual nudging is to monitor, and if necessary, adjust the residual norm of a state estimate in the observation space so that it does not exceed a pre-specified threshold. We suggest a rule to choose the pre-specified threshold, and construct a state estimate accordingly to achieve this objective. Numerical experiments suggest that introducing residual nudging to a particle filter may (substantially) improve its performance, in terms of filter accuracy and/or stability against divergence, especially when the particle filter is implemented with a relatively small number of particles.

physics.ao-ph↗
arXiv2013

Saharan mineral dust outbreaks observed over the North Atlantic island of La Palma in summertime between 1984 and 2012

We estimate the frequency of Saharan mineral dust outbreak events observed over the North Atlantic island of La Palma based on in situ nightly atmospheric extinction measurements recorded almost continuously since 1984 by the Carlsberg Meridian Telescope at the Roque de los Muchachos observatory. The outbreak frequency shows a well-defined seasonal peak in the months of July to September, during which time the occurrence of Saharan dust events (SDEs) is approximately 28\pm6%. We find considerable year-to-year variability in the summertime SDEs frequency, observing a steady reduction between 1984 and 1997, followed by a period of relative mean stability from 1999 to 2012. We investigated changes in the atmospheric extinction of the SDEs as an indicator of strength of the episodes and found that this parameter approximately follows the SDE frequency, however, instrumental limitations prevented us from deriving precise conclusions regarding their long-term changes. A lagged correlation analysis between SDE properties and the El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Sahel rainfall index (SRI) was performed. We found that 55\pm4% of the year-to-year variations in July-September SDE frequency may be reproduced by a lagged relationship to the NAO conditions during the preceding October-December period, and 45\pm4% may be reproduced by a negative correlation to the SRI during the preceding February-April period. Based on these relationships it may be possible to obtain an approximate indication of the strength of the upcoming summertime dust season over the North Atlantic around half a year in advance.

physics.ao-ph↗
arXiv2014

On the concept of exergy and available enthalpy: Application to atmospheric energetics

The available enthalpy is an early form of the modern thermodynamic concept of exergy, which is the generic name for the amount of work obtainable when some matter is brought to a state of equilibrium with its surroundings by means of reversible processes. It is shown in this paper that a study of the hydrodynamic properties of available enthalpy leads to a generalization of the global meteorological available energies previously introduced by Lorenz, Dutton and Pearce. A local energy cycle is derived without approximation. Moreover, static instabilities or topography do not prevent this theory from having practical applications. The concept of available enthalpy is also presented in terms of the potential change in total entropy. Using the hydrostatic assumption, limited-area energetics is then rigorously defined, including new boundary fluxes and new energy components. This innovative approach is especially suitable for the study of energy conversions between isobaric layers of an open limited atmospheric domain. Numerical evaluations of various energy components are presented for a hemispheric field of zonal-average temperature. It is further shown that this new energetic scheme realizes a hierarchical partition of the components so that the smallest of those available enthalpy reservoirs are almost of the same magnitude as the kinetic energy. This is actually the fundamental property that induced Margules to define the primary concept of available kinetic energy in meteorology.

physics.ao-ph↗
arXiv2014

On the concept of pseudo-energy of T. G. Shepherd. A close link with exergy functions

Shepherd (1993) derived a general expression for the available potential energy for compressible, hydrostatic flow, where the sum of this available energy and the kinetic energy is called pseudo-energy. He demonstrated that for a special basic state the small-amplitude limit of the generalized available potential energy reduces to the well-known approximate form of Lorenz (1955) expressed in a pressure vertical coordinate. Other forms of available energies exist in atmospheric energetics and in thermodynamics, where the name exergy has been coined by Rant (1956) to denote the maximum work that can be extracted from any system when it is subject to some constraints (adiabatic transformations or constant total energy for instance). The purpose of this note is to show that the specific available enthalpy function denoted by $a_h = (h - h_r) - T_r \: (s - s_r)$ in Marquet (1991) can be obtained from the generalized approach of Shepherd for another basic state. This special form of pseudo-energy also leads to the global hydrostatic concepts of Dutton (1973) and Pichler (1977). The function $a_h$ only depends on the specific enthalpy $h$ and entropy $s$ at any point, the values $h_r$ and $s_r$ refer to the special dead state at temperature $T_r$ and pressure $p_r$. It is explained that for another choice of the basic state, the generalized expression of Shepherd reduces with a good accuracy to the approximate functions introduced by Pearce (1978) or Blackburn (1983) in meteorology and Thomson (1853, Lord Kelvin) in thermodynamics.

physics.ao-ph↗
arXiv2014

Definition of a moist-air entropy potential temperature. Application to FIRE-I data flights

A moist entropy potential temperature -- denoted by $θ_{s}$ -- is defined analytically in terms of the specific entropy for moist air. The expression for $θ_{s}$ is valid for a general mixing of dry air, water vapour and possible condensed water species. It verifies the same conservative properties as the moist entropy, even for varying dry air or total water content. The moist formulation for $θ_{s}$ is equal to the dry formulation $θ$ if dry air is considered and it verifies new properties valid for the moist air cases, both saturated or under-saturated ones. Exact and approximate versions of $θ_{s}$ are evaluated for several Stratocumulus cases, in particular by using the aircraft observations FIRE-I experiment data sets. It appears that there is no (or small) jump in $θ_{s}$ at the top of the PBL. The mixing in moist entropy is almost complete in the PBL, with the same values observed in the clear air and the cloudy regions, including the very top of the entrainment region. The Randall-Deardorff CTEI analysis may be interpreted as a mixing in moist entropy criterion. The iso-$θ_{s}$ lines are plotted on skew $T$-$\ln(p)$ and conserved variable diagrams. All these properties could suggest some hints on the use of moist entropy (or $θ_{s}$) in cloud modelling or in mixing processes, with the marine Stratocumulus considered as a paradigm of moist turbulence.

physics.ao-ph↗